Ms. Lan Chang Profile

Lan Chang

Student at Beijing Univ of Chemical Technology

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Publications (2)

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Proceedings Article | 24 October 2017 Paper

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

Lan Chang, Mengmeng Zhang, Wei Li

Proceedings Volume 10461, 104610J (2017) https://doi.org/10.1117/12.2283229

KEYWORDS: Medical imaging, Hyperspectral imaging, Image classification, Image segmentation

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A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Proceedings Article | 24 October 2017 Paper

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

Qiong Ran, Lan Chang, Wei Li, Xiaofeng Xu

Proceedings Volume 10461, 1046102 (2017) https://doi.org/10.1117/12.2281268

KEYWORDS: Image classification, Hyperspectral imaging, Medical image processing

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Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

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